Skip to content
New issue

Have a question about this project? Sign up for a free GitHub account to open an issue and contact its maintainers and the community.

Sign up for GitHub

By clicking “Sign up for GitHub”, you agree to our terms of service and privacy statement. We’ll occasionally send you account related emails.

Already on GitHub? Sign in to your account

Remove nolint tags #252

Merged
merged 1 commit into from
May 20, 2024
Merged

Remove nolint tags #252

Show file tree
Hide file tree
Changes from all commits
Commits
Show all changes
1 commit
Select commit
File filter

Filter by extension

Filter by extension
Conversations
Failed to load comments.
Loading
Jump to
Jump to file
Failed to load files.
Loading
Diff view
Diff view
2 changes: 0 additions & 2 deletions R/simulate.r
View file
Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -63,7 +63,6 @@
#' * `time` - the time of infection.
#' @author James M. Azam, Sebastian Funk
#' @export
#nolint start
#' @details
#' # Definition of a transmission chain
#' A transmission chain as used here is an independent case and all
Expand Down Expand Up @@ -106,7 +105,6 @@
#' \code{simulate_*(..., generation_time = function(n){rlnorm(n, 0.58, 1.38)})}
#' OR \code{simulate_*(..., generation_time = \(n){rlnorm(n, 0.58, 1.38)})},
#' where `...` are the other arguments to `simulate_*()`.
#nolint end
#' @examples
#' # Using a Poisson offspring distribution and simulating from an infinite
#' # population up to chain size 10.
Expand Down
4 changes: 2 additions & 2 deletions vignettes/design-principles.Rmd
View file
Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -41,7 +41,7 @@ Additionally, the package provides mixture probability distributions for generat

### Simulation functions

Simulation of branching processes are achieved through `simulate_chains()` and `simulate_chain_stats()`. For details of the underlying methods, see the [theory vignette](https://epiverse-trace.github.io/epichains/articles/theoretical_background.html). #nolint
Simulation of branching processes are achieved through `simulate_chains()` and `simulate_chain_stats()`. For details of the underlying methods, see the [theory vignette](https://epiverse-trace.github.io/epichains/articles/theoretical_background.html).

The simulations are stochastic, meaning that one set of inputs can produce varied results. The models here can also be use to explore scenario analyses by varying the inputs. Often, in cases where there is need for more than one run of the model and/or with more than one set of parameter values, the inputs and outputs are stored in separate data structures. However, this approach can be limiting when performing scenario analyses, as the user has to manually manipulate the two objects with reshaping and joining operations. This has the potential to lead to errors and loss of information. Hence, `simulate_chains()` and `simulate_chain_stats()` return objects of the dedicated classes `<epichains>` and `<epichains_summary>` respectively that store the input parameters and the output of the simulation in a single object.

Expand All @@ -62,7 +62,7 @@ In summary, an `<epichains>` object has the following structure:
* `generation` (`<integer>`), and optionally,
* `time` (`<numeric>`), if `generation_time` is specified

* Attributes (See definitions in [simulate_chains](https://epiverse-trace.github.io/epichains/reference/simulate_chains.html)): #nolint
* Attributes (See definitions in [simulate_chains](https://epiverse-trace.github.io/epichains/reference/simulate_chains.html)):
* `n_chains`,
* `statistic`,
* `stat_threshold`,
Expand Down
Loading